With the widespread use of digital consumer electronic capturing devices such as digital cameras and camera phones, the size of consumers' image collections continue to increase very rapidly. Automated image management and organization is critical for easy access, search, retrieval, and browsing of these large collections.
A method for automatically grouping images into events and sub-events based on date-time information and color similarity between images is described in U.S. Pat. No. 6,606,411 B1, to Loui and Pavie (which is hereby incorporated herein by reference). An event-clustering algorithm uses capture date-time information for determining events. Block-level color histogram similarity is used to determine sub-events. This method has the shortcoming that clustering very large image sets can take a substantial amount of time. It is especially problematic if events and sub-events need to be recomputed each time new images are added to a consumer's image collection, since additions occur a few at a time, but relatively often. Another problem is that consumers need to be able to merge collections of images distributed across multiple personal computers, mobile devices, image appliances, network servers, and online repositories to allow seamless access. Recomputing events and subevents after each merger is inefficient.
The event-clustering algorithm described in U.S. Pat. No. 6,606,411 B1 has the limitation that it uses date-time information from digital camera capture metadata. This is problematic if images to be added to a database lack correct date-time information. Examples of such images include scanned images, digital image CDs from film capture, stills from video camcorders, or images from digital cameras with incorrect date-time settings. In many cases, the images have an associated date-time that relates to origination of a digital file after scanning or other processing, rather than date-time of image capture.
Many methods based on content-based image classification have been proposed for images where no metadata is available. In PCT Patent Application WO 01/37131 A2, published on May 25, 2001, visual properties of salient image regions are used to classify images. In addition to numerical measurements of visual properties, neural networks are used to classify some of the regions using semantic terms such as “sky” and “skin”. The region-based characteristics of the images in the collection are indexed to make it easy to find other images matching the characteristics of a given query image. U.S. Pat. No. 6,240,424 B1, issued May 29, 2001, discloses a method for classifying and querying images using primary objects in the image as a clustering center. Images matching a given unclassified image are found by formulating an appropriate query based on the primary objects in the given image. U.S. Pat. No. 6,477,269 B1, issued Nov. 5, 2002, discloses a method that allows users to find similar images based on color or shape by using an example query. It is known to provide image retrieval from image databases using a variety of techniques. U.S. Pat. No. 6,480,840, to Zhu and Mehrotra, issued on Nov. 12, 2002, discloses content-based image retrieval using low-level features such as color, texture and color composition.
These content-based methods have the shortcoming of not considering another type of information commonly available with images, chronological order. Images are commonly stored on media in chronological order. For example, images on a Kodak PictureCD™ derived from film capture are in order of capture. Filenames are often created for images using a numerical sequence or other sequence that results in a chronology. For example, some captured digital images have numerical suffixes in the filename that indicate order of generation.
It would thus be desirable to provide methods and systems, in which new images are additively clustered in a database, without reclustering the entire database.
It is further desirable to provide methods and systems, in which chronological order can be considered in additive clustering.